A fully automatic curve localization method for extracted spine

The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Aishu Xie, Ervin Gubin Moung, Xu Zhou, Zhibang Yang
التنسيق: مقال
اللغة:English
English
منشور في: Yogyakarta: Institute of Advanced Engineering and Science (IAES) 2024
الموضوعات:
الوصول للمادة أونلاين:https://eprints.ums.edu.my/id/eprint/41932/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41932/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41932/
http://doi.org/10.11591/ijece.v14i4.pp4018-4033
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id my.ums.eprints.41932
record_format eprints
spelling my.ums.eprints.419322024-11-18T03:16:01Z https://eprints.ums.edu.my/id/eprint/41932/ A fully automatic curve localization method for extracted spine Aishu Xie Ervin Gubin Moung Xu Zhou Zhibang Yang Q1-390 Science (General) QA440-699 Geometry. Trigonometry. Topology The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii) custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located. Yogyakarta: Institute of Advanced Engineering and Science (IAES) 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/41932/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/41932/2/FULL%20TEXT.pdf Aishu Xie and Ervin Gubin Moung and Xu Zhou and Zhibang Yang (2024) A fully automatic curve localization method for extracted spine. International Journal of Electrical and Computer Engineering (IJECE), 14 (4). pp. 1-16. ISSN 2722-2578 http://doi.org/10.11591/ijece.v14i4.pp4018-4033
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic Q1-390 Science (General)
QA440-699 Geometry. Trigonometry. Topology
spellingShingle Q1-390 Science (General)
QA440-699 Geometry. Trigonometry. Topology
Aishu Xie
Ervin Gubin Moung
Xu Zhou
Zhibang Yang
A fully automatic curve localization method for extracted spine
description The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii) custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located.
format Article
author Aishu Xie
Ervin Gubin Moung
Xu Zhou
Zhibang Yang
author_facet Aishu Xie
Ervin Gubin Moung
Xu Zhou
Zhibang Yang
author_sort Aishu Xie
title A fully automatic curve localization method for extracted spine
title_short A fully automatic curve localization method for extracted spine
title_full A fully automatic curve localization method for extracted spine
title_fullStr A fully automatic curve localization method for extracted spine
title_full_unstemmed A fully automatic curve localization method for extracted spine
title_sort fully automatic curve localization method for extracted spine
publisher Yogyakarta: Institute of Advanced Engineering and Science (IAES)
publishDate 2024
url https://eprints.ums.edu.my/id/eprint/41932/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/41932/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/41932/
http://doi.org/10.11591/ijece.v14i4.pp4018-4033
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score 13.250246